Publications

Hsin-Yuan Huang (Robert) is an Assistant Professor of Theoretical Physics at Caltech. His research explores the intersection of quantum computing, information theory, and machine learning, with a focus on understanding how scientists, machines, and quantum computers can learn and discover new phenomena in our quantum-mechanical universe. His pioneering work includes developing classical shadow tomography for large-scale quantum systems, creating provably efficient machine learning algorithms for quantum many-body problems, and establishing quantum advantages in learning from experiments.

Huang's research findings have been published in prestigious journals including Nature, Science, Nature Physics, Nature Review Physics, and Physical Review Letters, as well as premier theoretical computer science conferences such as FOCS and STOC. His contributions have earned him numerous accolades, including the Milton and Francis Clauser Doctoral Prize from Caltech, awarded to research demonstrating exceptional originality and potential for opening up new avenues of human thought and endeavor. He has also received the Ben P. C. Chou Doctoral Prize in Information Science and Technology, a Google Ph.D. Fellowship, the Boeing Quantum Creator Prize, and is recognized as the William H. Hurt Scholar.

Huang has delivered over 120 research talks in the past five years. His work spans multiple disciplines, integrating concepts from quantum information theory, quantum many-body physics, learning theory, and complexity theory. He develops rigorous frameworks for understanding quantum systems, pushing the boundaries of what machines can learn and discover about our quantum universe. His research aims to harness the power of quantum mechanics to advance scientific discovery beyond the current capabilities of classical approaches.